Artificial intelligence
AI strategy
Identify AI applications that make sense for your business before choosing tools or models.
- Opportunity and dependency map
- Data, risk and feasibility assessment
- Pilot scope and acceptance criteria
Services
AI-native development for businesses
AI integrated into the product experience, authorised data and business workflows from the planning stage.
What AI-native development means in your project
We specialise in AI-native solutions: we design the role of AI together with the product journey, authorised sources, integrations and evaluation criteria. It may power a copilot, a knowledge assistant, an automation or a feature within your SaaS.
The first conversation identifies the task, the data available and the expected outcome. We can start with discovery and a prototype or assess an existing product. Quality, permissions, human oversight and operating costs are part of the scope.
Artificial intelligence
Agents and copilots
Assistants that support teams and agents that connect steps, with defined tools, permissions and oversight.
- Conversation design and tool contracts
- API integrations and MCP where appropriate
- Permissions, approval and exception handling
Artificial intelligence
Knowledge and RAG
Search and assistants connected to your company's authorised documents and data, with references to check the answers.
- Source curation and update pipeline
- Semantic or hybrid search for the context
- Answers with references and access control
Artificial intelligence
Intelligent automation
Document interpretation, classification and workflows between systems, with explicit rules for reducing manual steps.
- Workflow and exception mapping
- Information extraction and classification
- Integrations, queues and idempotent execution
Artificial intelligence
AI in digital products
AI features for platforms, applications and SaaS, designed as part of the product and user journey.
- Discovery and experience prototypes
- Generation, search or analysis within the product
- Data contracts and backend authorisation
Artificial intelligence
Operations and governance
Evaluations, observability and limits to monitor AI applications over time.
- Evaluation sets and regression tests
- Prompt, source and model versioning
- Quality, latency and usage metrics
Questions and answers
Where should an AI project start?
Start with a concrete task and the people who perform it. In the first conversation, we align goals, available sources, integrations and success criteria to define a clearly scoped pilot.
Do I need to train my own model?
Not always. We can evaluate existing models, retrieval-augmented generation (RAG) and integrations. Fine-tuning or proprietary models are considered when there is a technical justification, suitable data and a budget to maintain that choice.
Can AI use my company’s data?
Only authorised sources and operations should be part of the project. We define purpose, permissions, retention and provider requirements before connecting real data, with care for personal and confidential information.
Can an AI solution make mistakes?
Yes. We define tests, operational limits and review paths. Answers with references help people check the result; critical actions may require approval. We do not promise complete accuracy or unrestricted autonomy.
Can it integrate with my existing system?
We evaluate APIs, connectors, data contracts and system permissions. Feasibility depends on those conditions. The proposal explains dependencies, licences and any necessary adaptations.
How much does it cost and how long does it take?
Scope, data, integrations and operational requirements determine effort and timing. Model usage, infrastructure and third-party services are considered separately, with agreed assumptions and limits.
Do you work with a single AI provider?
The architecture starts with project requirements. Quality, privacy, latency, cost and the ability to switch inform the choice. Technology mentions do not imply partnerships or certifications.